Multichannel InSAR elevation reconstruction method based on dual-stream network

Abstract

This paper presents a multichannel InSAR elevation reconstruction method based on deep learning, where a dual-stream network consisting of an elevation reconstruction stream and a boundary detection stream, named as ERSBDS, is built to reconstruct elevation maps for observed terrains, from multiple interferograms. First, the elevation reconstruction stream adopts a modified DeepLabV3+ architecture, in which the Xception network is replaced by the lightweight network called MobileNetV3 in the encoder for not only reducing the network parameters but also maintaining the performance of the network, and then a spatial attention module is added to the encoding and decoding path to enhance the network's attention to the spatial information of feature maps. Second, the boundary detection stream is mainly composed of residual blocks, which can detect the boundary information of observed terrains and merge it into the elevation reconstruction stream to improve the accuracy of elevation reconstruction for observed scenes. Finally, a suitable data set is constructed to enable the trained network to accurately reconstruct elevation maps for observed scenes. The experiments for multichannel InSAR elevation reconstruction for observed scenes demonstrate the effectiveness of the proposed method, and show the advantages of this method in the accuracy and efficiency of elevation reconstruction, compared with some of the most commonly used methods.